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机构地区:[1]卫奇塔州立大学,美国堪萨斯67260 [2]中国矿业大学,江苏徐州221116
出 处:《山西电力》2011年第5期1-5,共5页Shanxi Electric Power
摘 要:根据采用递归合并模式划分策略来启发式地考虑电容器动作次数约束,将时间段的无功优化近似解耦为若干子时间段的无功优化,子时间段数目即为约束的动作次数,挖掘并定义子时间段的期望特征网络,结合期望特征网络,建立物理意义更为明确的子时间段多目标无功优化模型,并采用自适应遗传算法来求解此无功优化模型,所有子时间段无功优化模型求解完毕即得到原无功优化模型的解。仿真算例验证了优化模型以及求解思路、算法的有效性。The recursive merging strategy is adopted for considering action time constraints of capacitor.By using this method,the original reactive power optimization in period is decomposed into reactive power optimization in several sub-periods.The number of sub-periods is the times of the action constrained.By mining and defining the expected marked network of sub-period,the multi-objective reactive power optimization model is built,and the model is solved based on self-adaptive genetic algorithm.When the solutions of all reactive power optimization models of sub-periods are completed,the solution of original reactive power optimization model is obtained.The simulation studies have verified the effectiveness of the model and its algorithm.
关 键 词:配电网 无功优化 动作次数约束 期望特征网络 自适应遗传算法
分 类 号:TM744[电气工程—电力系统及自动化]
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